Training data are developed for an ANN controlling a laboratory scale HPC. Special
attention is given to the development of a cost function to determine the optimal state
of the HPC for a particular input state. The cost function uses the reactive power
compensation efficiency (77Q), the distortion compensation efficiency (rid) and the
losses in the HPC (PLHP0 as optimisation parameters. After a process of optimisation
the ANN is trained with a randomised training set of size 2000. A 5:10:6 ANN
topology representing 5 input layer neurons, 10 hidden layer neurons and 6 output
layer neurons is used. The optimisation results in shorter training times as well as
more effective training.
A laboratory scale experiment, which practically proves that an ANN can make
meaningful choices in terms of HPC control, is conducted. The adaptive behaviour of
the ANN controller for the HPC is evaluated by means of the interactive integrated
state space model. It is found that the ANN controller can sensibly adapt its output
under conditions of line impedance change as well as conditions of load changes of
users sharing the same point of coupling as the consumer being compensated.
The conclusion from this research is that it is viable to apply AI in the control of an
HPC. A non-linear, time-varying system such as this ideally lends itself to the
application of ANN control. The total cost of the HPC is expected to be minimised
while minimum standards in terms of compensation are still maintained. The
performance of such an ANN controller is however strongly dependent on the
integrity of the training data. Using an actual system to set up the training data would
be the ideal in refining the ANN model. Devising a strategy to continually update the
training of the ANN to ensure the relevancy with respect to the dynamic range of the
ANN is recommended as an area for further research.